Method for the prediction of individual disease course in sepsis

ABSTRACT

The invention relates to the use of gene expression profiles, obtained in vitro from a patient sample, for the generation of criteria for the prediction of an individual course of disease in sepsis. The invention is further of use for determining the probability of survival in sepsis, the assessment of the course of disease in sepsis during treatment and for the classification of sepsis patients.

CROSS REFERENCE TO RELATED APPLICATION

This application is a national stage of PCT/EP2005/00336 filed Jan. 14,2005 and based upon DE 10 2004 015 605.0 filed Mar. 30, 2004 under theInternational Convention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the use of gene expression profilesobtained from a patient sample in vitro for setting up criteria for theprognosis of the individual course of disease in sepsis, a method for invitro measurement of such gene expression profiles as well the use ofthe gene expressions profiles and/or of probes used therefore forswitching of and/or for changing the activity of target genes and/or fordetermining the gene activity for screening of active agents againstsepsis and/or for evaluating the effect in the treatment of sepsisand/or the quality of the active agent and/or the integrity of theactive agent in cellular and cell-free sepsis model systems and insepsis animal models.

The present invention further relates to new possibilities of predictingthe probability of surviving and the development of lethal complicationsin sepsis patients which can be derived from experimentally verifiedinsights in conjunction with the occurrence of changes in gene activity(transcription) in patients with sepsis.

2. Description of Related Art

Despite advances in pathophysiological understanding and the supportivetreatment of intensive care patients, generalised inflammatoryconditions as SIRS and sepsis as defined according to American Collegeof Chest Physicians/Society of Critical Care Medicine ConsensusConference (ACCP/SCCM) of 1992 [1] frequently occur in patients inintensive care facilities and contribute substantially to mortality ofinfections [2-3]. The mortality rate is approximately 20% in the case ofSIRS, approximately 40% in the case of sepsis, and increases to up to70-80% in the case of development of multiple organ dysfunctions [4-6].The contribution of SIRS and sepsis to morbidity and lethality is ofmultidisciplinary interest, as it increasingly puts the success of themost advanced or experimental treatment methods of many medicinal fields(e.g. traumatology, neurosurgery, cardiac/pulmonary surgery, visceralsurgery, transplantation medicine, hematology/oncology, etc.) at a risk,as they all, without exception, are threatened by an increased risk ofthe development of SIRS and sepsis. This also becomes apparent from thecontinuous increase of the occurrence of sepsis, namely by 139% from73.6 to 176 cases per 100,000 hospital patients from 1979 and 1977, forexample [7]. Thus, the decrease of morbidity and lethality of manyseriously ill patients goes along with the concurrent progress inprevention and treatment and especially detection and monitoring of theprogression of the sepsis and severe sepsis.

On a molecular basis, sepsis is defined as a clinical picture caused bypathogenic microorganisms. On the basis of an exhaustion of molecularcontrol and regulation mechanisms near the infection site, a generalisedinflammatory reaction develops that affects the whole organism. Thisinfection is responsible for the clinical symptoms/criteria fordiagnosis/SIRS-criteria according to [1] confirmed by the physician.This generalised inflammatory condition (defined as sepsis according to[1]) goes along with signs of the activation of various cell systems(endothelial cells, but also all leucocyte-like cell systems and inparticular of the monocyte/macrophage system). Finally, molecularmechanisms, the normal task of which it is to protect the host againstinvasive microorganisms, harm the host's own organs/tissues and, thus,essentially contribute to the development of the organ dysfunctionsdreaded by the physicians [8-11].

The meaning of the term sepsis has changed considerably in the course oftime. An infection, or the strong suspicion of an infection, are stillan essential part of the current definition of sepsis. Of particularconsideration is, however, the description of organ failure functionsremote from the location of infection in the framework of theinflammatory host reaction. The criteria of the American College ofChest Physicians/Society of Critical Care Medicine Consensus Conference(ACCP/SCCM) of 1992 are the ones that became most accepted ininternational literature as definition of the term sepsis [1]. Accordingto these criteria [1] distinctions are made between the clinicallydefined degrees of severity “systemic inflammatory response syndrome”(SIRS), “sepsis”, “severe sepsis” and “septic shock”. According to thisdefinition, SIRS is defined as the systemic response of the inflammatorysystem to an infectious or non-infectious stimulus. At least two of thefollowing clinical criteria must be satisfied in this context: Fever>38°C. or hypothermia<36° C., a leukocytosis>12 G/l or a leukopenia<4 G/lor, as the case may be, a shift to the left in the differentialhaemogram, heart rate>90/min, tachypnoea>20 breaths/min or PaCO2(partial pressure of carbon dioxide in arterial blood)<4.3 kPa. Thoseclinical conditions which satisfy the SIRS criteria and for whichcausatively an infection can be confirmed our at least is very probableare defined as sepsis. A severe sepsis is characterized by theadditional occurrence of organ dysfunctions. Common organ dysfunctionsinclude changes in the state of consciousness, oliguria, lactateacidosis or sepsis-induced hypotension with a systolic blood pressurelower than 90 mmHg, or drop in pressure of more than 40 mmHg from theinitial value, respectively. If such a hypotension cannot be treated bythe administration of crystalloids and/or colloids and the patientfurther needs treatment with catecholamines, then one refers to this asseptic shock. Such a septic shock is detected in about 20% of all sepsispatients.

Sepsis is the clinical result of complex and highly heterogeneousmolecular processes, which are characterized by the involvement of manycomponents and their interactions on every organizational level of thehuman body: genes, cells, tissues, organs. The complexity of theunderlying biological and immunological processes resulted in many kindsof studies comprising a wide range of clinical aspects. One of theresults from these studies was that the evaluation of new sepsistherapies is rendered more difficult due to the presently used criteriawhich are quite unspecific and clinical based and which do notsufficiently show the molecular mechanisms [12]. Due to a lack ofspecificity of the currently used diagnosis of sepsis and SIRS, theclinicians are actually not sure from which point of time a patient isto be treated with a specialised therapy, for example with antibiotics,which for their part can have substantial side effects [12]. A surveycarried out by the European Society of Intensive Care Medicine (ESICM)showed that 71% of the physicians questioned were unsure regarding thediagnosing of sepsis, despite many years of clinical experience [22].

Ground-breaking discoveries in molecular biology and immunology duringthe last two decades have allowed the development of a deepenedunderstanding of sepsis that is oriented more towards the basicmechanisms. The knowledge resulting therefrom regarding relevant targetshas formed the basis for the development of targeted and adjuvanttherapeutic concepts basing primarily on the neutralisation of essentialsepsis mediators [13-16]. One cause for the failure of almost allimmunomodulatory approaches of therapy in clinical trials—despiteeffectivity in animal experiments—is considered to be the merely thepoor correlation between the clinical diagnostic criteria, that arerather symptom oriented, and the basic mechanisms of a generalisedimmuno response [12, 17-18].

This is not surprising in retrospect, as even healthy humans may showchanges of the heart rate and the respiratory rate, respectively, in thecourse of their daily activities, which per definition already wouldallow the diagnosis of SIRS. In consideration of today's biomedicalpossibilities, it seems anachronistic that 751 000 patients in the USAare diagnosed, classified and treated according to the above mentionedACCP/SCCM criteria per year. Prominent authors thus have already longcriticized that, at the expense of an improved sepsis diagnosis, in thepast decade too much energy and financial resources have been lavishedon the search for a “magic bullet” for sepsis therapy [19]. Further,recently published expert's opinions also indicate that for a betterpathophysiologic understanding of sepsis a modification of the consensuscriteria according to [1] is necessary [20-21]. Furthermore, manyphysicians agree that the consensus criteria according to [1] do notcorrespond to a specific definition of sepsis. A survey carried out bythe European Society of Intensive Care Medicine (ESICM) showed that 71%of the physicians questioned are not sure about when to diagnose asepsis, despite long-term experience in the clinical field [22].

Due to the above mentioned problems in the application of the consensuscriteria according to [1], critical care physicians discuss variousproposals for a more sensitive and specific definition of the variousdegrees of severity of sepsis [2, 23]. The new feature here is the factthat molecular changes should be taken into account directly in theevaluation of the severity of a sepsis as well as in the inclusion ininnovative methods of treatment of sepsis (as for example the therapywith activated recombinant protein C). This consensus process [23],which currently is being carried backed by five internationalprofessional associations, is at the current time long from completion.It is the object to establish a system for the evaluation of theseverity of sepsis. By means of this system it should become possible toclassify the reactions of patients on the basis of their predisposedconditions, the kind and extend of the infection, the kind and severityof the host response as well as the degree of the accompanying organdysfunction. The system described is referred to as PIRO, theabbreviation of the English terms “predisposition”, “insult infection”,“response” and “organ dysfunction”. From this, the individual chance ofsurvival as well as the potential responding to the therapy may bederived [23]. Likewise, non-infectious conditions, which are currentlysubsumed under the concept of SIRS according to [1] are to be classifiedmore precisely corresponding to the individual degree severity of SIRS.

Studies are carried out to find biomarkers which reflect the severity ofthe SIRS on the molecular level, as well, and render a cleardiscrimination of infectious conditions possible (currently classifiedas sepsis according to [1]). Similar classifications of the stages arealready successfully used in other medical fields, for example for theclassification of the different disease in oncology (TNM System, [24]).

In comparison to the consensus criteria according to [1], additionalmolecular parameters are to be included into the diagnosis [23] in orderto render an improved correlation of the molecularinflammatory/immunologic host response with a degree of severity of thesepsis, and additionally, the making of statements on the individualprognosis possible. At the present time various scientific andcommercial groups are intensively searching for such molecularbiomarkers, since the conventional parameters, such as, for example, thedetermination of the C-reactive protein or the procalcitonin do not meetall clinical requirements and are, in particular, only insufficiently inthe position to differentiate between surviving and nonsurviving sepsispatients [25]. Due to the insufficient specificity and sensitivity ofthe consensus criteria according to [1] and due to the faulty or delayeddetection of the cause of infection, there is need for innovativediagnostic processes that are supposed to improve the capability of theperson skilled in the art to prognosticate the course of disease withsepsis at an early point of time, to make it comparable in the clinicalcourse and to make statements on the individual prognosis and theresponse to specific treatments.

Technological advances, in particular the development of microarraytechnology, make now it possible for the person skilled in the art tosimultaneously compare 10 000 or more genes and their gene products. Theuse of such microarray technologies can now provide informationregarding the status of health, regulatory mechanisms, biochemicalinteractions and signal transmitter networks. As the comprehension howan organism reacts to infections is improved this way, this shouldfacilitate the development of enhanced modalities of detection,diagnosis and therapy of sepsis disorders.

Microarrays have their origin in “southern blotting” [10], whichrepresented the first approach to immobilizing DNA-molecules so that itcan be addressed three-dimensionally on a solid matrix. The firstmicroarrays consisted of DNA-fragments, frequently with unknownsequence, and were applied dotwise onto a porous membrane (normallynylon). Routinely, cDNA, genomic DNA or plasmid libraries were employedand the hybridized material was labelled with a radioactive group[27-29].

Recently, the use of glass as substrate and fluorescence for detectiontogether with the development of new technologies for the synthesis andfor the application of nucleic acids in very high densities made itpossible to miniaturize the nucleic acid arrays. At the same time, theexperimental throughput and the information content were increased[30-32].

Further, it is known from WO 03/002763 that microarrays basically can beused for the diagnosis of sepsis and sepsis-like conditions.

The first explanation for the applicability of microarray technology wasobtained through clinical trials in the field of cancer research. Here,expression profiles proofed to be valuable with regard to identificationof activities of individual genes or groups of genes, which correlatewith certain clinical phenotypes [33]. Many samples of individuals withor without acute leukaemia or diffuse B-cell lymphoma were analyzed andgene expression labels (RNA) were found and subsequently employed forthe clinically relevant classification of these types of cancer [33,34]. Golub et al. found out that an individual gene is not enough tomake reliable predictions, while, however, predictions based on thechange in transcription of 53 genes (selected from more than 6000 genes,which were present on the arrays) are highly accurate [33].

Alisadeh et al. [34] examined large B-cell lymphomas (DLBCL). Expressionprofiles were worked up by the authors with a “lympochip”, a microarraybearing 18 000 clones of complementary DNA that was developed to monitorgenes that are involved in normal and abnormal development oflymphocytes. By using cluster analysis, they managed to classify DILBCLin two categories that showed great differences with regard to thesurvival chance of patients. The gene expression profiles of thesesubtypes correlated to two significant stages of the B-celldifferentiation.

Yeoh E. et al. described the use of gene expression profiles establishedby means of microarrays for the prognosis of the course of leukaemia[35]. From various other studies with cancer patients, further examplesfor the use of gene expression profiles for the prediction of the chanceof survival are known [36-38]. These studies show that the probabilityof recurrence can be predicted by the measurement of the gene expressionprofiles. This would be of high clinical-medicinal importance as, on theone hand, it would be possible to pay more attention to these patients,for example by means of more appointments for consultation during tumorafter-treatment. This way, an early diagnosis of relapse as well as asystematic treatment of same would be possible. On the other hand, fewerappointments would be necessary for patients with a gene expressionprofile that does not point to an increased risk of relapse. Further,such gene expressions would also be usable in the decision whether atumor has to be treated more or less aggressively. As a result, theauthors suggested to regularly creating gene expression profiles fromcancer patients for an early identification of the individual risk fortherapy-related complications in the patient [35].

The use of gene expression profiles for the prediction of the individualcourse of disease in sepsis and in particular for the prediction of theindividual risk of development of lethal complications have not yet beendescribed.

The German Patent Applications DE 103 36 511.7, DE 103 150 31.5 and 102004 009 952.9 which have not yet been prepublished, describe that geneexpression profiles are, in principle, usable, for example by means ofmicroarray technology for the diagnosis of SIRS, generalisedinflammatory inflammations, sepsis and severe sepsis. These applicationsare herein incorporated by reference.

DETAILED DESCRIPTION OF THE INVENTION

For the creation of gene expression profiles according to the presentinvention, a majority of specific genes and/or gene fragments is used,selected from the group consisting of SEQ-ID No. 1 to SEQ-ID No. 247, aswell as gene fragments thereof with 5-2000 or more, preferably 20-200,more preferably 20-80 nucleotides.

These sequences with the sequence ID 1 to SEQ-ID No. 247 areincorporated by the scope of the present invention and they are indetail disclosed in the enclosed sequence listing of 56 pages and 247sequences which is, thus, part of the description of the presentinvention and, therefore, also part of the disclosure of the invention.In the sequence listing the single sequences are further assigned to theSEQ-ID No. 1 to SEQ-ID No. 247 to their GenBank Accession No. (website:http://www.ncbi.nlm.nih.gov/).

The present invention further relates to the use of gene expressionprofiles, which are obtained in vitro from a patient sample, and/or ofprobes used for this purpose, selected from the group consisting ofSEQ-ID No. 1 to SEQ-ID No. 247 as well as gene fragments therefrom withat least 5-2000, preferably 20-200, more preferably 20-80 nucleotides,for switching off and/or for changing the activity of target genesand/or the determination of the gene activity for the screening ofactive agents for sepsis and/or for assessing the effect on sepsisand/or the quality of the active agent and/or the integrity of theactive agent in cellular and cell-free sepsis model systems and insepsis animal models.

In this context, also hybridisable synthetic analogues of the listedprobes may be used.

Further, the gene activities in sepsis patients can be determined in abiologic fluid and from this “value” conclusions may be drawn withregard to the course of disease, the chance of survival, the course oftherapy or the possibility to include or exclude the sepsis patients inclinical trials.

Another embodiment of the invention is characterized in that a specificgene and/or gene fragment is selected from the group consisting ofSEQ-ID No. 1 to SEQ-ID No. 247, as well as gene fragments thereof with5-2000 or more, preferably 20-200, more preferably 20-80 nucleotides.

Another embodiment of the present invention is characterized in that atleast 2 to 100 different cDNAs are used.

Another embodiment of the present invention is characterized in that atleast 200 different cDNAs are used.

Another embodiment of the present invention is characterized in that atleast 200 to 500 different cDNAs are used.

Another embodiment of the present invention is characterized in that atleast 500 to 1000 different cDNAs are used.

Another embodiment of the present invention is characterized in that atleast 1000 to 2000 different cDNAs are used.

Another embodiment of the present invention is characterized in that thegenes or gene fragments and/or the sequences derived from their RNA arereplaced by synthetic analogues, aptamers, as well as peptide nucleicacids.

Another embodiment of the invention is characterized in that thesynthetic analogues of the genes comprise 5-100, in particular approx.70 base pairs.

Another embodiment of the present invention is characterized in that thegene activity is determined by means of hybridisation methods.

Another embodiment of the present invention is characterized in that thegene activity is determined by means of microarrays.

Another embodiment of the invention is characterized in that the geneactivity is determined by hybridisation-independent methods, inparticular by enzymatic and/or chemical hydrolysis and/or amplificationmethods, preferably PCR, subsequent quantification of nucleic acidsand/or of derivates and/or fragments of same.

Another embodiment of the present invention is characterized in that thesample is selected from: body fluids, in particular blood, liquor,urine, ascitic fluid, seminal fluid, saliva, puncture fluid, cellcontent, or a mixture thereof.

Another embodiment of the present invention is characterized in thatcell samples are optionally subjected a lytic treatment, in order tofree their cell contents.

It is obvious to the person skilled in the art that the individualfeatures of the present invention can be combined with each other in anydesired way.

The term marker genes as used in the present invention encompasses allderived DNA-sequences, partial sequences and synthetic analogues (forexample peptido-nucleic acids, PNA). The description of the inventionreferring to the determination of the gene expression on RNA level isnot supposed to be a restriction but only an exemplary application ofthe present invention.

The description of the invention referring to blood is only an exemplaryembodiment of the present invention. The term biological liquids as usedin the present invention encompasses all human body fluids.

Further advantages and features of the present invention will becomeapparent from the description of an embodiment.

Embodiment

Studies on Differential Gene Expression in Sepsis for DifferentiationBetween Nonsurviving and Surviving Patients.

Whole blood samples of 28 patients who were under the care of a surgicalintensive care unit were examined for the measurement of thedifferential gene expression in connection with sepsis in order todifferentiate between nonsurviving and surviving patients.

Whole blood samples of 12 surviving (9 male and 3 female patients) and16 deceased patients (13 male and 3 female patients) were drawn duringtheir whole stay in intensive care (patient samples). In the time periodin the intensive care unit, each of these patients developed a sepsisthe degree of severity being different. Gene expression profiles wereanalysed from those patients samples that have been drawn at the firstday of treatment with the most severe degree of sepsis according to [1]during the first sceptical complications (some patients in intensivecare suffer from more than one sepsis during the course of treatment).

A range of characteristics of the patients suffering from sepsis isshown in table 1. In the table, information regarding age, sex, cause ofsepsis (cp. diagnosis) as well as clinical severity on the date ofadmission to the intensive care unit, measured according to in clinicalliterature well documented APACHE-II-Scores, as well as on the date offirst treatment, measured according to in clinical literature welldocumented SOFA-Scores (both scores respectively in dots) were given.Likewise, the plasma protein level of procalcitonin (PCT), a more recentsepsis marker, from the samples of which the gene expression profileswere drawn at the date of treatment and the individual survival statusare indicated.

As control samples, the total RNA from the cell lines SIG-M5 were used.All of the patient samples were co-hybridised with the control sample onone microarray each.

TABLE 1 Data of the group of patients APACHE-II SOFA ClassificationScore Score PCT Survival Patient Age Gender Diagnosis according to [1][Points] [Punkte] [ng/ml] State 1 48 femal acute cholezystitis(gangrenous) severe sepsis 17 8 0.76 survived 2 71 male otheraspergillosis of the lung sepsis 13 6 1.69 survived 3 57 maleinsufficiency of the mitral valve III septical shock 11 11 186 survived4 77 femal peritonitis severe sepsis 11 10 4.1 survived 5 50 femalinfection of patchplastic prothesis AIC on both severe sepsis 9 9 1.71survived 6 33 male polytrauma septical shock 9 9 0.65 survived 7 83 malestenosis of the aortic valve septical shock 33 14 1.15 survived 8 60male prolapse of an intervertebral disc c6/7 severe sepsis 21 11 0.86survived 9 45 male polytrauma at traffic accident severe sepsis 11 61.18 survived 10 20 male polytrauma after traffic accident septicalshock 29 16 202 survived 11 67 male catheter sepsis severe sepsis 22 794.49 survived 12 42 male epidural bleeding severe sepsis 15 14 0.46survived 13 53 femal acute pancreatitis (necrot.) severe sepsis 20 112.16 died 14 56 male acute posterior transmural myocardial septicalshock 21 9 2.16 died infarction 15 64 femal intracranial bleeding(non-traumatic) septical shock 14 12 500 died 16 70 male septical shockseptical shock 27 17 9.42 died 17 78 femal acute peritonitis septicalshock 27 10 29.2 died 18 70 male septical shock septical shock 10 151.02 died 19 72 male malignant neoformation of the cardia septical shock10 12 4.92 died 20 88 male choledochal bile stone without cholangitis orseptical shock 18 7 11.6 died cholezystitis, bile-duct obstruction notstated 21 63 male intestinal perforation (non-traumatic) septical shock29 10 9.17 died 22 74 male respiratory insufficiency, not specifiedseptical shock 22 13 12 died 23 66 male acute peritonitis in intestinalseptical shock 13 8 9.05 died perforation (non-traumatic) 24 64 malecardiac disease, not specified, cardiac severe sepsis 35 9 405 diedarrest with successful reanimation 25 73 male dissection of the aorta,thoracic septical shock 19 12 123 died 26 61 male pleural empyema, rightside septical shock 11 6 1.64 died 27 62 female acute peritonitisseptical shock 27 16 16.1 died 28 73 male intestinal pneumonia,respiratory septical shock 32 11 10.9 died insuffiency, not specified,acute myocardial infarction

Experimental Description:

After drawing whole blood, the total RNA of the samples was isolatedusing the PAXGene Blood RNA kit according to the manufacturer's (Qiagen)instructions.

Cell Cultivation

For cell cultivation (control samples) 19 cryo cell cultures (SIGM5)(frozen in liquid nitrogen) were used. The cells were each inoculatedwith 2 ml Iscove's medium (Biochrom AG) supplemented with 20% fetal calfserum (FCS). Subsequently, the cell cultures were incubated in 12 wellplates for 24 hours at 37° C. in 5% CO2. Subsequently, the content ofthe 18 wells was parted in 2 parts with the same volume so that finally3 plates of the same format (36 wells in total) were available.Afterwards, the cultivation was continued under the same conditions for24 hours. Afterwards, the resulting cultures of 11 wells of each platewere combined and centrifuged (1000×g, 5 min, ambient temperature). Thesupernatant was removed and the cell pellet was dissolved in 40 ml ofthe above mentioned medium. These 40 ml of dissolved cells weredistributed in equal shares in two 250 ml flasks and incubated afteradding 5 ml of the above-mentioned medium. 80 μl of the remaining 2 mlof the two remaining plates were placed in empty wells of the sameplates that had previously been prepared with 1 ml of theabove-mentioned medium. After 48 hours of incubations, only one of the12 well plates was processed as follows: 500 μl were extracted from eachwell and combined. The resulting 6 ml were introduced into a 250 mlflask comprising approximately 10 ml of fresh medium. This mixture wascentrifuged for 5 minutes with 1000×g at ambient temperature anddissolved in 10 ml of the above-mentioned medium. The following resultswere obtained by the subsequent cell counting: 1,5×107 cells per ml, 10ml total volume, total number of cells: 1.5×108. As the number of cellswas not yet sufficient, 2.5 ml of the above-mentioned cell suspensionwas introduced into 30 ml of the above-mentioned medium in a 250 ml (75cm2) flask (4 flasks in total). After 72 hours of incubation 20 ml offresh medium were added to each flask. After the subsequent incubationof 24 hours, the cells were counted as described above. The total amountof cells was 3.8×10⁸ cells. In order to obtain the desired number ofcells of 2×106 cells, the cells were resuspended in 47.5 ml of the abovementioned medium in 4 flasks. After the incubation time of 24 hours, thecells were centrifuged and washed two times with phosphate buffer inabsence of Ca²⁺ and Mg²⁺ (Biochrom AG).

The isolation of the total RNA is performed by means of NucleoSpin RNA LKits (Machery&Nagel) according to the manufacturer's instructions. Theabove described process was repeated until the necessary number of cellswas obtained. This was necessary to obtain the necessary amount of 6 mgtotal RNA corresponding to an efficiency of 600 μg RNA per 108 cells.

Reverse Transcription/Labelling/Hybridisation

Subsequently, the complementary cDNA was prepared from the total RNA ofthe patient and control samples by means of reverse transcription undersubstitution of the dTTP-Fraktion by synthesizedaminoallyl-deoxyuridintriphosphate (AA-dUTP). The RNA/cDNA complex wastransformed into single strand cDNA by means of RNA hydrolysis.Subsequently, the cDNA samples were labelled with the fluorescent dyesCy3 and Cy5 (Amersham) by means of chemical binding to AA-dUTP.

A microarray from the company SIRS-Lab was used for the hybridisation ofthe samples. On the microarray used, 5308 different polynucleotides withlengths of 55 to 70 base pairs were immobilised. Each of thepolynucleotides represents a human gene. The spots were immobilised witha multitude of different control spots within 28 subarrays, each of thesubarrays being arranged in a grid of 15×15 spots. The hybridisation wascarried out using the hybridisation station HS 400 (Tecan) according tothe manufacturer's instructions. The hybridisation solution was composedof 3.5×SSC (1×SSC comprises 150 mM NaCl and 15 mM sodiumcitrate), 0.3%SDS, 25% formamide, 0.8 μg/μl of each cot-1 DNA, yeast tRNA and polyAand the respective cDNA-samples. The arrays were hybridised for 10.5hours at 42° C. The subsequent washing procedure is carried out asfollows:

Addition of washing buffer I (2×SSC, 0.003% SDS) to the hybridisationchamber, washing at ambient temperature for 1.5 minutes, addition ofwashing buffer II (1×SSC) to the hybridisation chamber, washing withwashing buffer for 1.5 minutes at ambient temperature, adding of washingbuffer III (0.2×SSC) to the hybridisation chamber, washing with washingbuffer III for 1.5 minutes at ambient temperature. Subsequently, thesurfaces of the microarrays were dried with nitrogen at a pressure of2.5 bar for 2.5 minutes at 30° C.

The hybridisation signals of the processed microarrays were subsequentlyread by means of the GenePix 4000B (Axon) scanner and the expressionratios of the different expressed genes were determined by means of theGenePix Pro 4.0 (Axon) software.

Analysis

For the analysis, the average intensity of one spot was determined asmedian value of the corresponding spot pixel.

Correction of Systemic Errors:

The median of the pixel of the local background was subtracted from themedian of the spot pixel. For all further computations, the signal wastransformed by means of arcus sinus hyperbolicus. The normalizationoccurred according to the approach of Huber et al. [39]. According tothis approach, the additive and the multiplicative bias in a microarraywas estimated on the basis of 70% of the gene samples present. For theanalysis, the transformed relative ratios of the signals of the patientssamples were calculated with respect to the control. This means that thecalculation for the gene no. j (j=1, . . . , 5308) of the patient no. nrevealed the data Gj,n=arcsin h(Scy5(j,n))−arcsin h(Scy3(j,n)), wherein[SCy3(j,n), SCy5(j,n)] is the associated signal pair. When a spot couldnot be analyses for a patient (no detectable signal intensity in bothchannels), the associated value was marked as “missing value”.

Statistical Comparison:

For comparison the paired random student test was employed per gene.Both random samples contained the values of the patient groups ofsurviving and nonsurviving patients, respectively. For choosing thedifferentially expressed genes, the associated p-value and the number ofmissing values were evaluated.

Results:

It applied for the group of the selected genes that the associatedp-value was smaller than 0.05, with at least 5 detectable signalsreceived per patient group.

The criterion for the grading of the examined genes was the level of theexpression ratio of each gene. The most overexpressed or underexpressedgenes, respectively, in the surviving and nonsurviving patients were theinteresting ones.

Table 2 shows that 65 genes of the patient sample were found, which weresignificantly overexpressed in the nonsurviving patients, if comparedwith the surviving patients. Furthermore, Table 3 shows that 182 genesof the nonsurviving patients were significantly under-expressed, ifcompared with the surviving patients. From the results it is clear thatthe gene activities listed in Table 2 and Table 3 distinguish betweensurviving and nonsurviving sepsis patients (corresponding to sepsisaccording to [1]). Thus, the listed gene activities provide markers forthe prediction of the chance of survival and the development of lethalcomplications in sepsis patients.

TABLE 2 Significant elevated gene activities in samples of patients withsepsis according to [1], indicated as their relative relationship to thecorresponding gene activities of nonsurviving patients suffering fromsepsis according to [1]. mean normalised and transformed expressionvalue Standard deviation Group of Group of Group of Group of GenBanksurviving nonsurviving surviving nonsurviving Difference Accession No.p-value patients patients patients patients of mean values Seq-Id.AI560533 0.036 −0.42 −0.24 0.24 0.17 0.17 84 D49410 0.042 −0.40 −0.220.14 0.24 0.18 125 AI263527 0.047 −0.49 −0.32 0.12 0.26 0.18 67 AI7329580.028 −0.25 −0.06 0.10 0.25 0.18 102 AI679923 0.033 −0.45 −0.27 0.190.23 0.19 97 R56877 0.034 −0.30 −0.10 0.19 0.23 0.20 215 AA478621 0.008−0.30 −0.10 0.16 0.19 0.20 24 R93174 0.038 −0.36 −0.15 0.25 0.24 0.21222 AA935872 0.030 −0.35 −0.14 0.14 0.28 0.21 46 H22921 0.016 −0.32−0.10 0.16 0.24 0.21 132 R61687 0.046 −0.67 −0.46 0.13 0.31 0.22 218N73694 0.040 −0.38 −0.16 0.25 0.26 0.22 153 H28119 0.040 −0.25 −0.030.22 0.27 0.22 134 AI697430 0.046 −0.46 −0.24 0.24 0.29 0.22 99 AI6310760.043 −0.81 −0.58 0.19 0.31 0.23 92 N63777 0.038 −0.51 −0.28 0.28 0.260.23 150 AA889648 0.045 −0.36 −0.13 0.19 0.32 0.23 40 AA679067 0.039−0.30 −0.07 0.33 0.22 0.23 30 AI865298 0.012 −0.34 −0.10 0.15 0.26 0.24113 H14986 0.028 −0.06 0.18 0.19 0.31 0.24 129 AI033361 0.014 −0.36−0.11 0.17 0.27 0.24 54 AI149817 0.012 −0.42 −0.17 0.30 0.15 0.25 60AI269981 0.043 −0.22 0.03 0.27 0.31 0.25 68 NM_003082 0.043 −0.70 −0.450.20 0.33 0.25 168 AI620645 0.040 −0.41 −0.16 0.26 0.32 0.25 91 R065850.022 −0.48 −0.22 0.22 0.29 0.25 204 H61046 0.023 −0.41 −0.15 0.31 0.240.26 137 AI803880 0.022 −0.46 −0.20 0.22 0.30 0.26 106 AI862880 0.047−0.71 −0.45 0.26 0.35 0.26 111 AI185721 0.033 −0.58 −0.32 0.32 0.28 0.2662 AI888234 0.040 −0.53 −0.27 0.23 0.35 0.26 114 AA278821 0.048 −0.28−0.01 0.31 0.32 0.26 7 AI933967 0.042 −0.66 −0.40 0.16 0.39 0.27 121AI635650 0.026 −0.63 −0.37 0.24 0.31 0.27 93 R89075 0.032 −0.59 −0.320.29 0.31 0.27 221 H14100 0.018 −0.53 −0.25 0.20 0.31 0.27 128 AA6993560.022 −0.22 0.06 0.21 0.33 0.27 32 NM_000678 0.012 −0.66 −0.39 0.17 0.290.27 186 AI579907 0.003 −0.41 −0.13 0.23 0.21 0.28 86 AA284108 0.009−0.46 −0.17 0.29 0.24 0.29 9 AA406037 0.030 −0.42 −0.13 0.16 0.33 0.2914 AA441939 0.007 −0.44 −0.14 0.24 0.25 0.30 18 H11718 0.020 −0.10 0.200.15 0.27 0.31 127 AA621333 0.036 −0.46 −0.14 0.23 0.40 0.33 29 AI8649190.035 −0.51 −0.18 0.25 0.42 0.33 112 AI184594 0.032 −0.51 −0.18 0.430.32 0.34 61 NM-001288 0.048 −0.28 0.07 0.45 0.37 0.35 188 XM-0438640.014 −0.54 −0.20 0.22 0.40 0.35 243 AI475085 0.026 −0.37 −0.01 0.310.31 0.36 80 H79760 0.027 −0.51 −0.15 0.33 0.34 0.36 139 XM-012039 0.047−1.22 −0.84 0.33 0.52 0.37 239 AI377802 0.021 −0.49 −0.11 0.39 0.39 0.3775 NM_021972 0.038 −0.94 −0.56 0.46 0.37 0.38 183 AI936462 0.022 −0.61−0.23 0.33 0.45 0.38 123 AA846117 0.040 0.00 0.40 0.45 0.40 0.40 39AI732878 0.032 −0.54 −0.13 0.61 0.31 0.41 101 M61199 0.031 −0.72 −0.290.36 0.48 0.43 143 NM_004513 0.007 −0.54 −0.11 0.35 0.38 0.43 176NM-012068 0.014 −1.17 −0.70 0.16 0.55 0.47 194 NM_000228 0.032 −1.21−0.68 0.61 0.55 0.53 156 NM_000629 0.014 −0.93 −0.39 0.41 0.51 0.54 158AI655693 0.040 −0.50 0.04 0.48 0.44 0.55 94 AA017133 0.030 −0.26 0.290.42 0.37 0.55 5 H16999 0.027 −0.55 0.07 0.43 0.45 0.62 130 AI8884930.024 −0.56 0.25 0.78 0.42 0.81 115

TABLE 3 Significant reduced gene activities in samples of patients withsepsis according to [1], indicated as their relative relationship to thecorresponding gene activities of nonsurviving patients suffering fromsepsis according to [1]. Mean normalised and transformed expressionvalue Standard deviation Group of Group of Group of Group of GenBanksurviving nonsurviving surviving nonsurviving Difference Accession No.p-value patients patients patients patients in mean values Seq-Id.AI147932 0.031 −0.02 −0.18 0.13 0.20 −0.17 58 R88267 0.036 0.08 −0.120.31 0.18 −0.21 220 AI039866 0.039 0.06 −0.15 0.34 0.14 −0.21 55AI583425 0.023 0.22 0.00 0.22 0.22 −0.22 87 AA845475 0.032 0.13 −0.100.28 0.22 −0.22 38 AA992381 0.049 −0.24 −0.47 0.27 0.29 −0.23 48NM_022740 0.047 0.21 −0.02 0.34 0.22 −0.23 184 R43258 0.025 0.11 −0.120.10 0.28 −0.23 208 N32057 0.022 −0.02 −0.25 0.20 0.25 −0.23 145AI277856 0.026 0.13 −0.10 0.18 0.24 −0.23 70 AA454150 0.028 −0.03 −0.260.30 0.21 −0.23 21 AI597793 0.002 0.15 −0.09 0.10 0.19 −0.24 89 AA0129110.009 0.32 0.08 0.20 0.19 −0.24 4 AA960982 0.037 0.28 0.03 0.37 0.20−0.24 47 AA906962 0.037 −0.27 −0.52 0.34 0.27 −0.26 43 AI933013 0.0420.40 0.14 0.38 0.24 −0.26 120 AA704293 0.012 0.37 0.11 0.30 0.18 −0.2633 AA682521 0.022 0.10 −0.16 0.31 0.25 −0.26 31 AI142427 0.013 0.24−0.03 0.20 0.29 −0.27 57 R54393 0.007 0.11 −0.15 0.32 0.12 −0.27 214NM_015318 0.012 0.22 −0.04 0.22 0.25 −0.27 196 AA528169 0.050 0.45 0.180.40 0.22 −0.27 27 XM-048792 0.049 0.21 −0.06 0.23 0.36 −0.27 244AI343613 0.039 −0.07 −0.34 0.34 0.27 −0.27 73 N91341 0.024 0.10 −0.180.27 0.27 −0.27 154 AI023785 0.020 0.06 −0.21 0.24 0.27 −0.27 53NM_032721 0.021 0.22 −0.05 0.26 0.25 −0.28 201 AA035159 0.044 0.08 −0.200.36 0.21 −0.28 6 AI091302 0.012 0.32 0.04 0.21 0.26 −0.28 56 AA8453720.040 0.56 0.28 0.29 0.35 −0.28 37 AI657063 0.005 0.19 −0.09 0.28 0.16−0.28 95 AI187962 0.005 0.55 0.27 0.22 0.23 −0.28 64 AA845015 0.018 0.02−0.27 0.31 0.27 −0.29 36 AA436651 0.042 0.28 0.00 0.46 0.23 −0.29 17NM_147180 0.028 0.22 −0.07 0.36 0.26 −0.29 202 AI421397 0.030 0.42 0.130.37 0.25 −0.29 77 W15233 0.034 0.35 0.05 0.22 0.31 −0.30 230 AA9062780.009 −0.16 −0.46 0.22 0.22 −0.30 42 AI187401 0.044 −0.07 −0.37 0.420.31 −0.30 63 AI492493 0.038 −0.16 −0.46 0.35 0.36 −0.30 81 AA4969690.001 0.16 −0.15 0.22 0.18 −0.31 26 AI936300 0.049 0.61 0.30 0.31 0.36−0.31 122 NM_002983 0.046 0.27 −0.04 0.38 0.34 −0.31 165 N53480 0.016−0.18 −0.49 0.16 0.29 −0.31 149 AI400066 0.009 −0.14 −0.45 0.34 0.24−0.32 76 AI799547 0.022 0.24 −0.08 0.22 0.35 −0.32 105 R94509 0.015 0.28−0.04 0.32 0.27 −0.32 223 AI539457 0.036 0.27 −0.05 0.37 0.38 −0.33 83AI921468 0.003 0.24 −0.09 0.30 0.22 −0.33 118 NM_005368 0.016 0.34 0.000.38 0.23 −0.34 247 NM_000597 0.026 0.12 −0.22 0.42 0.31 −0.34 185NM_005658 0.045 0.29 −0.05 0.59 0.22 −0.34 178 NM_014211 0.003 0.21−0.14 0.31 0.22 −0.34 195 AI342905 0.039 0.66 0.32 0.50 0.32 −0.35 72NM_006273 0.013 0.49 0.14 0.34 0.29 −0.35 179 H28769 0.010 0.34 −0.010.39 0.25 −0.35 135 AA460188 0.040 0.23 −0.11 0.48 0.31 −0.35 23 R427780.022 0.43 0.08 0.48 0.27 −0.35 206 NM_004740 0.027 0.21 −0.15 0.50 0.27−0.36 177 AA432083 0.011 0.14 −0.22 0.48 0.18 −0.36 16 AA397913 0.0260.86 0.50 0.33 0.42 −0.36 10 AI473446 0.041 0.40 0.04 0.39 0.29 −0.36 79AI289206 0.010 0.36 0.00 0.44 0.20 −0.36 71 NM_000074 0.018 0.43 0.060.42 0.30 −0.37 155 AA458848 0.012 0.07 −0.30 0.38 0.32 −0.37 22 R613950.008 0.47 0.10 0.30 0.28 −0.37 217 N39164 0.027 0.22 −0.15 0.41 0.24−0.37 147 T91937 0.032 0.54 0.16 0.42 0.31 −0.38 226 AA707013 0.025 0.01−0.37 0.51 0.29 −0.39 34 H20320 0.024 0.64 0.26 0.43 0.40 −0.39 131K03195 0.045 0.21 −0.18 0.41 0.46 −0.39 141 NM_001710 0.049 0.63 0.240.69 0.23 −0.39 189 AA281330 0.030 0.69 0.30 0.54 0.35 −0.39 8 AI8258900.049 0.32 −0.07 0.31 0.58 −0.39 109 AI270372 0.016 0.38 −0.01 0.51 0.24−0.40 69 T97352 0.037 0.39 −0.01 0.46 0.46 −0.40 229 AA002267 0.040 0.420.02 0.64 0.30 −0.40 1 AI561302 0.040 0.09 −0.31 0.42 0.37 −0.40 85AI741506 0.002 0.38 −0.03 0.14 0.28 −0.40 103 XM-012608 0.009 0.37 −0.030.45 0.29 −0.40 240 AA935686 0.012 0.39 −0.02 0.55 0.21 −0.41 45 R535640.015 0.46 0.06 0.40 0.26 −0.41 213 AI744597 0.016 0.28 −0.13 0.40 0.35−0.41 104 AI148036 0.035 0.30 −0.11 0.60 0.17 −0.41 59 AI925267 0.0360.41 0.00 0.63 0.34 −0.41 119 AA731720 0.007 0.31 −0.11 0.50 0.21 −0.4235 NM_021132 0.006 0.21 −0.21 0.34 0.34 −0.42 200

These changes characterized in Tables 2 and 3 can be used for theinventive process.

The GenBank Accession Numbers indicated in Tables 2 and 3(Internet-access via http://www.ncbi.nlm.nih.gov/) of the individualsequences are associated with the attached 56-page sequence listing,itemized or in detail with respectively one sequence (Sequence ID: 1 upthrough Sequence ID: 247).

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1-24. (canceled)
 25. A method for generating criteria for the predictionof an individual course of disease in sepsis, comprising obtaining abiological sample from a patient, preparing a gene expression profilefrom the sample in vitro, correlating data from the gene expressionprofile with observed or measured data regarding the course of diseasein sepsis for the individual from which the sample was obtained, andusing the gene expression profile to generate criteria for theprediction of the course of disease in sepsis for said individual. 26.The method as in claim 25, wherein prediction of the course of diseasein sepsis includes determining the probability of survival in sepsis.27. The method as in claim 25, wherein the course of disease in sepsisis determined during therapy.
 28. The method as in claim 25, whereinsaid gene expression profile is used to generate criteria forclassification of sepsis patients.
 29. The method as in claim 25,wherein said gene expression profile is used to generate inclusioncriterion or exclusion criterion of patients with sepsis in clinicaltrials of stages 2-4.
 30. The method as in claim 25, further comprisingusing said gene expression profile to generate gene activity data forfurther electronic processing.
 31. The method as in claim 30, whereinsaid further electronic processing comprises using the gene activitydata for the production of software for the description of theindividual prognosis of a sepsis patient, for diagnostic purposes and/orfor patient data management systems.
 32. The method as in claim 30,wherein the gene activity data is used for the production of expertsystems and/or for modelling of cellular signal transmission paths. 33.The method as in claim 25, including using a specific gene and/or genefragment for the generation of gene expression profiles, the gene and/orgene fragment being selected from a group consisting of SEQ-ID No. 1 toSEQ-ID No. 247 as well as gene fragments therefrom with at least 5-2000nucleotides.
 34. The method as in claim 33, wherein said gene fragmentscomprise 20-200 nucleotides.
 35. The method as in claim 33, wherein saidgene fragments comprise 20-80 nucleotides.
 36. A method for in vitromeasurement of gene expression profiles for generating criteria for theprediction of an individual course of disease in sepsis, comprising (a)determining the gene activity of various certain genes associated withsepsis in a patient sample, wherein the sepsis-specific genes and/orgene fragments are selected from the group consisting of: SEQUENCE-IDNo. 1 to SEQ-ID No. 247, as well as gene fragments thereof with 5-2000nucleotides, and (b) using the results of step (a) to produce a geneexpression profile, and (c) using the results of (b) to generatecriteria or the prediction of an individual course of disease in sepsis.37. The method as in claim 36, wherein said gene fragments comprise20-200 nucleotides.
 38. The method as in claim 36, wherein said genefragments comprise 20-80 nucleotides.
 39. The method according to claim36, wherein at least 2 to 100 different cDNAs are used.
 40. The methodaccording to claim 36, wherein at least 200 different cDNAs are used.41. The method according to claim 36, wherein 200 to 500 different cDNAsare used.
 42. The method according to claim 36, wherein at least 500 to1000 different cDNAs are used.
 43. The method according to claim 36,wherein at least 1000 to 2000 different cDNAs are used.
 44. The methodaccording to claim 36, wherein the genes or gene fragments and/or thesequences derived from their RNA listed in claim 10 are replaced bysynthetic analogues, aptamers, mirrormeres as well as peptide- andmorpholine nucleic acids.
 45. The method according to claim 44, whereinthe synthetic analogues of the genes comprise 5-100 base pairs.
 46. Themethod according to claim 36, wherein the gene activities are determinedby means of hybridisation methods.
 47. The method according to claim 46,characterized in that the gene activity is determined by means ofmicroarrays.
 48. The method according to claim 36, wherein the geneactivity is determined by hybridisation-independent methods, inparticular by enzymatic and/or chemical hydrolysis and/or amplificationmethods, preferably PCR, subsequent quantification of nucleic acidsand/or of derivates and/or of fragments of same.
 49. The methodaccording to claim 36, wherein the sample is selected from the groupconsisting of body fluids, in particular blood, liquor, urine, asciticfluid, seminal fluid, saliva, puncture fluid, cell content, or a mixturethereof.
 50. The method according to claim 36, wherein cell samples areoptionally subjected to lytic treatment, in order to free their cellcontents.
 51. A method for switching off and/or for changing theactivity of target genes and/or the determination of the gene activityfor the screening of active agents for sepsis and/or for assessing theeffect on sepsis and/or the quality of the active agent and/or theintegrity of the active agent in cellular and cell-free sepsis modelsystems and in sepsis animal models, said method comprising obtaining asample from a patient optionally determining the gene expression profilein vitro from the patient sample, and using the gene expression profileand/or the probes, selected from the group consisting of SEQ-ID No. 1 toSEQ-ID No. 247, that were used for the determination of the geneexpression profile, with at least 5-2000, preferably 20-80 nucleotidesfor switching off and/or changing the activity of target genes and/ordetermining the gene activity for the screening of active agents forsepsis and/or for assessing the effect on sepsis and/or the quality ofthe active agent and/or the integrity of the active agent in cellularand cell-free sepsis model systems and in sepsis animal models on thebasis of said gene expression profile.
 52. The method according to claim52, wherein hybridisable synthetic analogues of the probes listed inclaim 23 are used.